Downward approach to hydrological prediction

By Murugesu Sivapalan, Günter Blöschl, Lu Zhang, R. Vertessy. 2003

439 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/a6fdadd11adb0b93d2e9a3e72889d36b21dc6394

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

The evolution of process-based hydrologic models: Historical challenges and the collective quest for physical realism. (2017, neutral): “…to process-based hydrologic modeling [Wood et al. 1988; Grayson et al. 1992b, 1992a; Famiglietti and Wood 1995; Reggiani et al. 1998; Beven 2002; Sivapalan et al. 2003; Maxwell and Miller 2005; Wood et al. 2011; Beven and Cloke 2012; Wood et al. 2012], with the debate centered around issues…”
HESS Opinions : The complementary merits of competing modelling philosophies in hydrology (2017, background): “…towards achieving this may be the much advocated large sample, comparative hydrology and similarity analysis to identify patterns and generally applicable functional relationships (e.g. Sivapalan et al., 2003; McDonnell et al., 2007; Blöschl, 2013; Sivakumar et al., 2013; Gupta et al., 2014).”
What makes Darwinian hydrology "Darwinian"? Asking a different kind of question about landscapes (2014, background): “…effect of these properties at the watershed scale from the observed hydrologic signatures (e.g., Wittenberg, 1999; Jothityangkoon and Sivapalan, 2009; Kirchner, 2009), many derived from the notion of a “top-down” approach to conceptualizing hydrologic systems (Klemeš, 1983; Sivapalan et al., 2003).”
Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration (2013, background): “Realizing the potential of “reading the landscape” in a systems approach (cf. Sivapalan et al., 2003), Savenije (2010) argued that due to the co-evolution of topography, soil and vegetation, all of which define the hydrological function of a given location, an efficient, hydrologically meaningful…”
Hydrologic complexity and classification: a simple data reconstruction approach (2007, background): “Some recent and current studies in hydrology raise an increasing concern on the absence of such a framework or a classification system (Woods, 2002; Sivapalan et al., 2003; McDonnell and Woods, 2004).”
A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model (2008, background): “, 2002], until a desired level of model accuracy is achieved [see also Klemes, 1983; Sivapalan et al., 2003; Jothityangkoon et al., 2001; Atkinson et al., 2003; Fenicia et al., 2008].”
Hydrologic consistency as a basis for assessing complexity of monthly water balance models for the continental United States (2011, background): “Further work to untangle the reasons for model structure predominance can help to relate conceptual model structures to physical characteristics of the catchments, facilitating the task of prediction in ungaged basins [Sivapalan et al., 2003].”
Reconciling theory with observations: elements of a diagnostic approach to model evaluation (2008, background): “For other examples see Sivapalan et al. (2003b).”
Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty (2015, background): “Many studies have proposed parsimonious model structures simple enough to be identifiable from observations, to avoid overparameterization, reduce uncertainty, and subsequently improve reliability of a model (Hornberger et al., 1985; Hooper et al., 1988; Jakeman and Hornberger, 1993; Wagener et al.,”
Global water resources modeling with an integrated model of the social-economic-environmental system (2011, background): “Thus, individual model components are often relatively simple and replicate the behavior of key variables or processes [35], with model improvements occurring analogously to the ‘‘downward approach’’ [96] described above.”